The following description is provided to assist the understanding of the reader. None of the information provided or references cited is admitted to be prior art.
Rapid development in DNA microarray technology has resulted in the parallel generation of expression data of thousands of genes of multiple organisms under various experimental conditions and/or time points. These gene expression data are prone to noise and ambiguity and may be unequally sampled over time. On the other hand, time series or multiple condition gene expression data are often under-determined, involving high-dimensional genes with very few time-points or conditions. An apparent similarity of expression profiles between a pair of genes may actually denote an indirect co-regulation by other genes, or may be due to a mere coincidence involving no causal relationship.
A gene regulatory network is comprised of genes interacting with each other, and acting as a complex input-output system for controlling cellular functions. It represents a complex structure consisting of various gene products activating or repressing other gene products. Transcription factor (TF) is a protein that interacts directly with its target gene(s) (T) by up regulating or down regulating its gene expression, thereby resulting in activation or inhibition of the target gene(s). A gene regulatory sub-network, by contrast, is a portion of a gene regulatory network. Understanding of a gene regulatory network, which demonstrates TF-T relationship, is helpful in elucidating basic biochemical mechanisms. Its impact on fast and accurate generation of gene regulatory pathways and associated gene regulatory sub-networks, for specific organs or tissues, is evident. Such information can then be used for understanding of a disease mechanism and used for drug design.
Identification of interactions between genes based on gene expression is a complex process involving delineation of linear as well as non-linear and higher-order interactions. Commonly used similarity measures, such as correlation analysis, do not consider non-linear or higher-order effects between genes.